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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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A robust adaptive load frequency control for micro-grids.

Mohammad-Hassan Khooban1, Taher Niknam2, Frede Blaabjerg3

  • 1Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran; Department of Energy Technology, Aalborg University, Aalborg DK-9220, Denmark.

ISA Transactions
|August 1, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a robust load frequency control strategy for islanded microgrids using electric vehicles as battery storage. The novel approach enhances grid stability by integrating Vehicle-to-Grid technology with advanced fuzzy logic control.

Keywords:
Adaptive PI controlGeneral Type II Fuzzy LogicLoad frequency control (LFC)Micro-gridsModified Harmony Search Algorithm (MHSA)

Related Experiment Videos

Last Updated: Mar 17, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Renewable Energy Integration

Background:

  • Microgrids operating in islanded mode face challenges with unstable power generation from sources like wind and solar.
  • Traditional Battery Energy Storage Systems (BESS) are effective but costly solutions for power balancing.
  • Vehicle-to-Grid (V2G) technology offers a cost-effective alternative by utilizing Electric Vehicle (EV) batteries as a large-scale BESS.

Purpose of the Study:

  • To propose a novel, robust Load Frequency Control (LFC) strategy for islanded microgrids.
  • To investigate the integration and impact of Electric Vehicle (EV) batteries as a Vehicle-to-Grid (V2G) resource for microgrid stability.
  • To develop an adaptive tuning method for Proportional-Integral (PI) controllers using advanced fuzzy logic and optimization techniques.

Main Methods:

  • A new robust LFC strategy is introduced, incorporating the effects of EV batteries in V2G mode.
  • A combination of General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) is employed for adaptive PI controller tuning.
  • The performance of GT2FLS is enhanced using an α-plane representation, simplifying complex computations by composing them from Interval Type II Fuzzy Logic Systems (IT2FLS).

Main Results:

  • The proposed controller effectively manages load frequency deviations in islanded microgrids.
  • Simulations using real-world data from a Swedish offshore wind farm and UK solar radiation data validate the controller's performance.
  • The novel controller demonstrates superior or comparable performance against recent advanced controllers like Optimal Fuzzy-PI (OFPI) and Optimal Interval Type II Fuzzy-PI (IT2FPI).

Conclusions:

  • The developed robust LFC strategy effectively enhances the stability and reliability of islanded microgrids.
  • The integration of V2G technology presents a viable and cost-effective solution for power balancing in microgrids.
  • The proposed adaptive tuning method using GT2FLS and MHSA offers an effective approach for optimizing PI controllers in complex power systems.